Improving Credit Card Operations with Data Mining Techniques

Authors

Malini Krishnamurthi, California State University, Fullerton

Document Type

Article

Abstract

Consumer credit is ubiquitous and lending poses credit risk – the risk of economic loss due to the failure of a borrower to repay according to the terms of his or her contract with the lender. And so, managing credit risk entails estimating the potential ability of borrowers to repay their debts. Researchers have sought to identify factors that contribute to consumer risk, by using quantitative models. However, the presence of data mining techniques to identify credit risk cannot be ignored. There is a paucity of research to demonstrate the use of data mining techniques in this context, and such studies could be instructive to practitioners and academicians. This study fills that void. Using a data mining tool, this study shows that consumers can be segmented by their characteristics such as education, income, years on the job, and payment habits. The study showed that the rich were highly educated and always paid in full. Delinquency experiences were more frequent in the lower income segments. Knowledge about the risk of delinquency can be useful for lenders to price for credit risk and therefore to expand the reach of credit to consumers without having to compromise on profitability.

Recommended Citation

Krishnamurthi, Malini (2007) "Improving Credit Card Operations with Data Mining Techniques," Journal of International Technology and Information Management: Vol. 16: Iss. 4, Article 4.
DOI: https://doi.org/10.58729/1941-6679.1215
Available at: https://scholarworks.lib.csusb.edu/jitim/vol16/iss4/4